4 Reasons Why Organizations Fail to Utilize Data Efficiently

Collecting raw data and then converting it into useful information has become such an important part of business today, so why is it that many organisations fall short when it comes to utilising data efficiently?

We’re going to take a close look at the answer to that question but first let’s look at some of the basics.

The main goal of data analysis is to gain a comprehensive understanding of a vast amount of raw data, so the first step is to develop ways of transforming that data into something digestible. Thus, data analysis is born.

Analytics can take on many different faces with graphs, charts, and reports ranking as the most common. The key is to provide decision-makers with a method of obtaining relevant information at a glance. That way, they don’t have to go through a ton of raw data every time they need to make a decision.

Accurate analytical forms are an essential part of research – an important part that’s often underestimated. If you get this wrong, you’ll make bad decisions due to inaccurate data and will only confuse the direction of your business.

So, the overall question remains: why do so many small businesses fail to properly utilise data? Here are some of the biggest reasons.

#1: They Lack the Right Skillset

Small business owners have to wear a lot of different hats when it comes to running their business. One of the common misconceptions is that they are prepared to do this themselves. As entrepreneurs, we have met every other challenge without hesitation. So, what makes this different?

Data analysis requires a completely unique skillset. While a few small business owners will possess these skills, most don’t. So, they need to take a more rational approach by building a team to diversify strategic decision-making.

#2: They Fail to Assume Responsibility Early

This is perhaps the biggest reason why small businesses fail to utilise data efficiently. Many people underestimate something of value until it becomes essential, forcing them to play catch up. There is so much data generated every day that playing catch up with data can be overwhelming.

The key is to start as soon as possible. Develop methods for collecting the right data and then build systems for analysing it. Use machine learning to your advantage. Build your business plan around analytics. Don’t wait until you’re left in the dust by other businesses. Start now!

#3: Letting Their Presumptions Lead the Way

Using the scientific approach is a sound business strategy. Some up with a hypothesis, gather data, and analyse it in an effort to prove or disprove the hypothesis. The problem comes when entrepreneurs allow their presumptions to lead the way. You have to take an unbiased approach.

The problem comes when entrepreneurs allow their presumptions to lead the way. You have to take an unbiased approach.

Even small amounts of bias in collecting raw data can lead to a conclusion based on belief rather than fact. For example, it’s okay to hypothesise that investing more money in marketing will lead to more leads but when the data shows something else, it’s important to be willing to admit that you were wrong.

#4: Inaccurate Data Recording Methods

Analytics can be highly influenced by the way data is recorded. A common reason why a business fails to utilise data is because they failed to record it correctly.

Audio or visual recording that is later transcribed into the appropriate format.

Asking participants to take notes for their analytical team.

Surveys that are taken by a researcher.

Open and close ended surveys.

These are all good data collection methods but make sure that the appropriate steps are taken to ensure their accuracy. The key is to make sure that data is collected in the most efficient way possible. Quality should never be compromised.